An autonomous vehicle (AV), also known as a “self-driving car”, is a vehicle that is capable of operating automatically without human input. AVs may operate at varying levels of autonomy, such as through a relatively low-level-autonomy system, sometimes referred to as an advanced driver-assistance system (ADAS) which is currently utilized in the automotive industry, or a high-level-autonomy system, sometimes referred to as an automatic-driving system (ADS) which is utilized in “self-driving” vehicles. While AV technology is advancing rapidly, developing a fully autonomous vehicle is an interdisciplinary challenge that requires further advancement in a number of related technologies.
AVs may have many different types of software onboard, including autonomous driving systems, operating systems, user-selected infotainment applications, firmware, etc. These software applications may generally be updated several times through updated software versions, patches, etc. Over time, certain versions and/or combinations of software may result in unintended or adverse outcomes. For example, certain software applications and/or combinations of software applications may have interaction issues if the software applications are not updated regularly.
As AVs become more prevalent, minimizing risks associated with AVs becomes increasingly important. As discussed above, combinations of software within an AV “software ecosystem” (e.g., the entirety of software applications onboard the AV) may produce unexpected effects on AV performance. While these effects may be difficult to predict based upon data from a single vehicle, data from a large number of AVs may facilitate enhanced analysis. Although at least some known systems may aggregate data from multiple AVs, none of these systems are able to evaluate risk based upon interactions between AV software ecosystems and manage software for individual vehicles based on the elevated risk. Accordingly, there is a need for a centralized system capable of aggregating and analyzing data regarding software ecosystems and environmental conditions from a large number of AVs to detect interactions that may lead to adverse vehicle performance issues. Further, there is a need for the centralized system to identify the interactions that may lead to adverse vehicle performance issues in individual AVs.